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Abstract

Agent-based approaches to manufacturing scheduling and control have gained increasing attention in recent years. Such approaches are attractive because they offer increased robustness against the unpredictability of factory operations. But the specification of local coordination policies that give rise to efficient global performance and effectively adapt to changing circumstances remains an interesting challenge. In this paper, we present a new approach to this coordination problem, drawing on various aspects of a computational model of how wasp colonies coordinate individual activities and allocate tasks to meet the collective needs of the nest. We focus specifically on the problem of configuring machines in a factory to best satisfy (potentially changing) product demands over time. Wasp-like computational agents that we call routing wasps act as overall machine proxies. These agents use a model of wasp task allocation behavior, coupled with a model of wasp dominance hierarchy formation, to determine which new jobs should be accepted into the machine's queue. We show for simple factories that our multi-agent system achieves the desired effect. For a given job mix, the system converges to a factory configuration that maximizes overall performance, and as the job mix changes, the system adapts to a new, more appropriate configuration. We also show that our system is competitive to that of an agent-based system for the problem that has been successfully demonstrated in real-world practice, outperforming this prior system in its intended domain in several important respects.